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  1. NOAA Forecast Systems Laboratory 2004 Technical Review GPS-Met Observing Systems Presented by GPS-Met Observing Systems Branch NOAA Research Forecast Systems Laboratory Demonstration Division January 27, 2004

  2. Tech Review Agenda Topic Speaker Time Overview/Introduction: Seth Gutman 1330 - 1345 FSL/DD GPS Satellite Orbits: Peng Fang 1345 - 1400 SIO/SOPAC NWP Model Studies: Tracy Smith 1400 – 1415 FSL/FRD CIRA Break 1415 - 1430 Data Products & Services: Susan Sahm 1430 - 1445 FSL/DD Data Applications: Daphne Grant 1445 - 1500 FSL/DD Operational Transition: Kirk Holub 1500 - 1515 FSL/DD Concluding Remarks: Seth Gutman 1515 – 1520 Questions: 1520 – 1530

  3. Overview/Introduction • The GPS-Met project started in 1993 as a collaboration between FSL, UCAR and NCSU to determine if & how GPS could be used to measure atmospheric moisture. • It has evolved into a collaboration between FSL, other NOAA organizations, other federal, state and local government agencies, universities, and the private sector. • This level of cooperation has permitted us to develop, test and evaluate a new upper-air observing system for less than 10% of the Demonstration Division’s budget. • Major accomplishments include: specification of the observation accuracy and error covariance; co-development of real-time data processing techniques; verification of positive impact on Wx forecast accuracy; definition and exploration of new applications.

  4. Collaborations FSL FRD, TOD, AD, SDD, MD, A&R, ITS NOAA Research ETL, AL, AMOL, CMDL, PMEL, GLERL, SEC Other NOAA: NWS (NDBC, ER, SR, CR, WR, AR, NCEP), NOS (NGS, CO-OPS), NESDIS (ORA), NGDC, NCDC Federal Gov’t: DOT (FHWA), DHS (USCG), DOD (USN, USAF, USACE), NASA (LaRC, JPL, GFSC), DOE (ARM) Universities: SIO, UH, UCAR, MIT, H-SAO, OSU, Purdue, U. Calgary, USM, CU, CSU, LSU, and SuomiNet Other Gov’t: AZ (various), AKDOT, CO (various), FDOT, MDOT, MNDOT, OHDOT, OKDOT, NYDOT, NCDOT, TXDOT, VTDOT

  5. Supporting the NOAA Strategic Plan • GPS-Met observations contribute to 3 of 4 NOAA Mission Goals: - Understanding climate variability and change to enhance society’s ability to plan and respond; - Serving society’s needs for weather and water information; - Supporting the Nation’s commerce with information for safe, efficient, and environmentally sound transportation. • GPS-Met also contributes to 4 of 6 cross-cutting priorities essential to support NOAA mission goals: - integrated global environmental observation and data management systems; - sound, reliable state-of-the-art research; - international cooperation and collaboration; and - homeland security.

  6. Supporting the FSL Strategic Plan • FSL conducts applied meteorological R&D to create and improve short-term warning and weather forecast systems, models, and observing technologies. • Ground-based GPS-Met contributes to all of these activities by providing high accuracy moisture observations under all weather conditions to forecasters, modelers, and researchers. • The unique capabilities of FSL have enabled the GPS-Met observing system to be developed, tested, and validated in a relatively short period. • Positive impact on Wx forecast accuracy has been demonstrated and verified using the FSL-developed RUC. • Lessons learned from using FX-Net will be applied to AWIPS, as we build GPS-Met display applications for WFOs in collaboration with SDD and MD.

  7. Technology Transfer/Outreach • FSL transfers new scientific and technological advances to its clients, including the National Weather Service, Department of Defense, foreign weather forecasting agencies, and private interests. • To facilitate this, DD funded a modest outreach activity led by Sher Schranz of FSL/TOD and CIRA, with assistance from Rhonda Lange, Will von Dauster and John Osborne. • Joe Golden also provided assistance, especially with forecast offices. • Efforts concentrated on NWS, FHWA, and DOD users. • Ongoing cooperation with Patty Miller and the MADIS group has greatly expanded access to GPS-Met data and products.

  8. Since 2002 Tech Review • The original system architecture (GPS + collocated Sfc. Met + dedicated comms) was modified to enable us to use GPS sites without all of these attributes. • We created the backbone site vs. infill site distinction. • The number of sites in the network increased from 121 to 291. • The number of systems in the processing array went from 12 to 18. • Latency (mean time from the end of a 30-min session until PW is delivered) decreased from 20 minutes to 14 minutes. • The number of GPS sites within 50 km of an NWS UA site increased from 4 to 48.

  9. Total # Sites # Sites installed in FY History, Evolution & Critical Decisions Data Processing Latency 336 hrs Precise Orbit 36 hrs Rapid Orbit Arbitrary 0.3 hr Hourly Orbit SuomiNet

  10. 291 GPS-Met Sites + 38 waiting for positions GPS-Met Demonstration Network 121 GPS-Met Sites + 52 waiting for positions

  11. Since 2002 Tech Review • NASA Co-PI on Aqua/AIRS science team. Provided calibration-validation PW data to all investigators. - Participating in AIRS cal/val experiments with NESDIS/ORA.

  12. Since 2002 Tech Review • Participated in IHOP 2002. Provided real-time data and analysis to all investigators. - In collaboration with researchers at NESDIS and CIMMS, Dan Birkenheuer and I are studying the temporal observation error structure of GOES-8 PW retrievals during IHOP by comparing them with GPS and sondes at synoptic and asynoptic times.

  13. Since 2002 Tech Review • By comparing GPS-IPW and PW from rawinsondes, we discovered that it is possible for GPS to identify problematic moisture soundings with high POD and low FAR.

  14. Since 2002 Tech Review • We received modest funding from the Interagency GPS Executive Board to study the feasibility of using space and conventional Wx models to reduce the impact of atmospheric refractivity on high accuracy GPS positioning and navigation. UNB3 – WAAS Predictor GPS Estimation – DRV1

  15. NOAA Forecast Systems Laboratory GPS-Met Observing System 2004 Technical Review Impact of Imperfect Orbits onGround-Based GPS Atmospheric Sensing Application Peng Fang Scripps Orbit and Permanent Array Center (SOPAC) University of California San Diego January 27, 2004

  16. Outline • SOPAC Hourly Orbits • Factors Affecting Orbit Performance • Global Network Configuration • Observation Span and Data latency • Satellite Performance • Auxiliary Information • Reliability of Operational Facilities • Impact upon GPS/MET Applications • Solutions to Various Problems

  17. SOPAC Hourly Orbits • Sliding window technique • 24 hour data span (reason: maintain orbit continuity by fitting longer arc) • Rejecting under performing satellites using internal and external checking • Separate full constellation processing for resuming previously rejected satellites • Duplicated data acquisition from multiple sources

  18. SOPAC Internet FSL IGS Hourly Data Aux. Data Net 1 Production Hourly Orbits with 12h prediction Net 2 Orbits QC Net N Full Constellation Hourly 8h 24 h 30m 1h Data Collect =< 1h Proc.Time = 1h Application uses 2+h prediction

  19. Global Network Configuration When tracking network has large gaps (network holes), there will be very little or no data to fit the orbits over the paths above them. Thus the orbital characteristics could not be modeled well, resulting in poor prediction.

  20. IGS hourly sites of global tracking network Blue = to be used Red = Possible to use Green = temporarily unavailable

  21. Observation Span and Data latency • Longer span helps fitting longer orbital arc which in turn helps the orbit prediction. However the processing burden increases. One key parameter, “once per rev.”, could not be estimated well with short observation span. • Higher latency means effective processing data span decreased.

  22. Satellite Performance • Satellite reposition • Misbehaving satellites • Eclipsing • Reset • Higher general noise level

  23. Auxiliary Information • Earth Orientation Parameters (polar motion, UT) e.g.poor prediction, missing update -> biased orbits -> biased tropo. delay estimates. e.g. over shoot at bending • Global reference frame e.g.Earthquake or site configuration change (antenna/receiver/monument) on tightly constrained sites -> error goes into orbit

  24. Reliability of Operational Facilities • Hardware failure (most often: RAM, hard drive, power supply) • Data server overloading (shared scripts, executables, auxiliary files) • Processing node overloading (usually after network interruption) • Intranet interruption (e.g. DNS down) • Internet interruption (e.g. maintenance, unusual event)

  25. Impact upon GPS/MET Applications • Poor performing satellite included • Poor configuration of global tracking network used • Poor latency of data supply (less data to fit orbit, predicted orbit would not be good) • Late orbit delivery (not to show) note: this is different from previous point. Using predicted orbits up to 8 hours should be OK

  26. Experiment Setup • Reference set: IGS final orbits • GW1247 PRN24 (336) PRN31 (338,339) • Reduced satellite from 27/28 to 22 • Removed last 6 hour observation • Excluded 5 sites usually having latency problem Total number of solutions: 24x8x5x2

  27. Solutions to Various Problems • System redundancy • Increased session span • Independent check • Improvement in QC procedures • Reject satellite to be repositioned in advance • Have alternative orbits ready

  28. NOAA Forecast Systems Laboratory GPS-Met Observing System 2004 Technical Review Ongoing Assessment of GPS-IPW Impact on RUC Forecasts Tracy Lorraine Smith Forecast Research Division January 27, 2004

  29. RUC experiments for GPS impact • 60km RUC • 1998 – 2003 • Ongoing 3h cycles with and without GPS IPW assimilation • 20km RUC • 5-day experiment – May 2000 • 15-day experiment- February 2001 • Ongoing 1h assimilation cycles with and without GPS IPW assimilation, comparisons and statistics available http://waylon.fsl.noaa.gov/cgi-bin/ruc20/ruc20.cgi 33

  30. NOAA/FSL GPS network  291 stations Information from Seth Gutman, Seth.I.Gutman@noaa.gov http://gpsmet.noaa.gov Current accuracy - IPW - RMS error <1.5 mm - bias <0.25 mm (positive) Latency – 18 min, use ‘hourly orbit’ – from RUC requirement 00-30 min avg, available at +48 min

  31. Hourly Data for RUC60/RUC20 Data Type ~Number Frequency Rawinsonde (balloons) 80 /12h NOAA 404 MHz wind profilers 31 / 1h PBL (915 MHz) wind profilers 24 / 1h RASS virtual temperatures 10 / 1h VAD winds (WSR-88D radars) 110-130 / 1h Aircraft (ACARS) 1400-4500 / 1h Surface/METAR 1500-1700 / 1h Surface/Buoy 100-150 / 1h Surface/Mesonet 2500-4000 / 1h GOES cloud-drift winds 1000-2500 / 1h GOES precipitable water 1500-3000 / 1h GPS precipitable water 278 / 1h SSM/I precipitable water 1000-4000 / 6h GOES cloud-top pressure/temp 10km res / 1h Ship reports/dropsondes as available Much competing data for GPS-IPW over US 35

  32. Optimal interpolation analysis for precipitable water obs - 2-d analysis of PW (ob – bkg) - percentage correction applied to water vapor mixing ratio at all levels Unavoidable problem – aliasing, esp. vert • PW ob errors • GPS 1 mm • GOES 3 mm • 1h forecast error • 5 mm • RUC20 PW changes • Account for station • vs. model terrain • difference • - no change • above 500 hPa • iterated solution • w/ PW, cloud, • in situ analysis

  33. Conclusions – RUC60 GPS impact tests • Multi-year study with the 60km RUC indicates that GPS-Met makes a small but consistent positive impact on short-term weather forecast accuracy: • primarily at the lower levels where most of the moisture resides - IPW more correlated w/ low-level moisture • magnitude of impact consistently increases with the number of stations • RH forecast improvement is greatest in the cool months when convection is less frequent and the moisture distribution is more synoptic scale. • impact on precipitation forecast accuracy generally increases with precipitation amount threshold No. Sta 18 56 67 100+ 200+ Level 1998-99 2000 2001 2002 2003 % improvement (normalized by total error) 850 1.5 3.8 3.9 5.0 5.4 700 1.1 4.1 6.3 6.5 7.0 500 0.7 2.1 2.0 2.4 3.1 400 0.3 0.1 -0.4 -0.5 1.0 Mean (850-400) 0.9 2.5 2.9 3.3 4.1 Mean (850-500) 1.1 3.3 4.1 4.6 5.2 Verification area

  34. Impact of GPS-IPW increases as the number of GPS observations increase Largest impact at 700 and 850 hPa, lower troposphere

  35. At 850 hPa there is a definite seasonal modality on the magnitude of the impact not seen at 700 hPa Monthly variation of GPS impact on 3h RH forecasts

  36. Impact of GPS on 3h RH forecasts verified against RAOBS at 00 and 12 UTC 01 Jan 03 - 31 Dec 03 Run by run verification shows impact can vary widely. Impact is greatly affected by weather regime.

  37. RUC experiments for GPS impact • 60km RUC • 1998 – 2003 • Ongoing 3h cycles with and without GPS IPW assimilation • 20km RUC • 5-day experiment – May 2000 • 15-day experiment- February 2001 • Ongoing 1h assimilation cycles with and without GPS IPW assimilation, comparisons and statistics available http://waylon.fsl.noaa.gov/cgi-bin/ruc20/ruc20.cgi 41

  38. IPW differences between 20km RUC analyses and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003 RMS Bias Without GPS RUC-GPS RMS Number 2103 2103 Mean (mm) 1.16 3.94 With GPS RUC-GPS RMS Number 2135 2135 Mean (mm) 0.25 2.36

  39. IPW differences between 20km RUC 3h forecasts and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003 Bias RMS Without GPS RUC-GPS RMS Number 2131 2089 Mean (mm) 3.07 3.92 With GPS RUC-GPS RMS Number 2131 2131 Mean (mm) 0.13 3.07

  40. IPW differences between 20km RUC 6h forecasts and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003 Bias RMS Without GPS RUC-GPS RMS Number 696 696 Mean (mm) 0.62 3.84 With GPS RUC-GPS RMS Number 2131 2131 Mean (mm) -0.06 3.40

  41. IPW differences between 20km RUC 9h forecasts and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003 Bias RMS With GPS RUC-GPS RMS Number 2131 2131 Mean (mm) -0.30 3.65 Without GPS RUC-GPS RMS Number 697 697 Mean (mm) 0.33 3.82

  42. IPW differences between 20km RUC 12h forecasts and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003 RMS Bias Without GPS RUC-GPS RMS Number 696 696 Mean (mm) 0.03 3.89 With GPS RUC-GPS RMS Number 709 709 Mean (mm) -0.48 3.91

  43. Difference RUC20 analysis with GPS minus RUC20 without GPS 9 Nov 2003 1500 UTC

  44. Time series of RUC IPW, GPS IPW, and RAOB IPW at Jacksonville, FL for 6 - 11 November 2003 FSL RUC with cold start from ETA is too dry, GPS-IPW can correct

  45. Bias and RMS for 20km RUC with and without GPS-IPW for 6-11 Nov 2003 + RUC without GPS-IPW + RUC with GPS-IPW Cold start for FSL RUC with GPS, GPS-IPW helps RUC to recover from dry bias, high RMS error 9 hrs before RAOBs

  46. Conclusions from RUC20 GPS impact studies • Impact on 3h RH forecasts similar to that from RUC60 • IPW forecast improvement evident out to 9 h • Interactive, ongoing assessment of GPS impact is • enhanced by the GPS/model comparison webpage • Future • Multi-week RUC20 retrospective impact tests • Assimilation into operational RUC20 at NCEP http://ruc.fsl.noaa.gov All FSL RUC forecasts (out to 48h) initialized with GPS-IPW